RCYC Bullish Bearish Indicator
Summary: The RCYC Bullish Bearish Indicator is a custom trading tool designed to help traders identify potential bullish and bearish conditions in the market using a combination of KDJ and RSI indicators. This indicator uses color-coded candles to visually represent bullish and bearish signals, making it easy to identify trend changes on the chart. The script is particularly useful for traders who prefer visual signals and want to incorporate both trend momentum (KDJ) and relative strength (RSI) in their analysis.
Description:
The RCYC Bullish Bearish Indicator is a unique mashup of the KDJ and RSI indicators, optimized to provide a clear visual representation of market conditions through color-coded candles. This indicator not only identifies the potential trend shifts but also provides alerts for significant crossover points, enhancing a trader's ability to make informed decisions.
How It Works:
KDJ Calculation:
The KDJ is a variation of the Stochastic Oscillator that includes the %J line, which can go beyond the typical 0-100 range of %K and %D.
The KDJ component of this indicator calculates the highest high and lowest low over a specified period (KDJ Length), using these values to derive the %K line.
The %D line is a smoothed version of %K, and the %J line is derived from %K and %D using the formula: J = 3 * %K - 2 * %D.
This indicator focuses on the behavior of the %J line in relation to a mid-point level (50), identifying crossovers and crossunders that signal potential shifts in market sentiment.
RSI Calculation:
The Relative Strength Index (RSI) is a momentum oscillator that measures the speed and change of price movements. It is widely used to identify overbought or oversold conditions.
In this indicator, RSI values are adjusted and plotted to align visually with the KDJ values, providing a complementary momentum analysis.
Crossover Logic and Candle Coloring:
The indicator tracks two main events:
CrossOver50: When the %J line crosses above the 50 level, indicating potential bullish momentum.
CrossUnder50: When the %J line crosses below the 50 level, indicating potential bearish momentum.
Depending on the crossover events, the script changes the color of the candles on the chart:
Red candles on the initial crossover above 50, followed by dark blue candles to maintain bullish sentiment.
Yellow candles on the initial crossover below 50, followed by light blue candles to maintain bearish sentiment.
Alerts:
The indicator includes alert conditions for both bullish and bearish signals:
Red Candle Alert: Notifies the trader when the %J line crosses above 50.
Yellow Candle Alert: Notifies the trader when the %J line crosses below 50.
These alerts allow traders to react promptly to key market signals without continuously monitoring the chart.
Usage and Benefits:
This indicator is designed for traders looking to combine momentum and trend analysis into a single visual tool. It is particularly useful for those trading in trending markets or looking for entry/exit signals based on momentum shifts.
The color-coded candles provide an intuitive way to assess market conditions at a glance, reducing the complexity associated with analyzing multiple indicators separately.
By integrating both KDJ and RSI, the RCYC Bullish Bearish Indicator offers a balanced approach to trend detection and momentum confirmation, making it versatile for various trading styles, including scalping, swing trading, and position trading.
Originality and Usefulness:
While the indicator builds upon the familiar concepts of KDJ and RSI, it uniquely merges them into a cohesive visual tool with distinct crossover-based alerts and candle coloring.
This approach makes the indicator original, as it simplifies the interpretation of complex signals into straightforward visual cues, enhancing the decision-making process for traders who prefer chart-based analysis.
Pengayun
Color Coded RSI [Phantom]Color Coded RSI
The Color Coded RSI enhances the standard RSI (Relative Strength Index) by applying dynamic color coding to the price bars, making it easier to visualize RSI levels directly on the chart.
Key Feature:
RSI-Based Color Coding: Price bars change color based on RSI values. High RSI values (above 70) show warm colors (red/orange), signaling potential overbought conditions, while low RSI values (below 30) display cool colors (blue), indicating possible oversold levels.
How to Trade with Color Coded RSI:
Overbought (Red/Orange Bars):
When the bars turn red or orange (RSI above 70), the market might be overbought. This could be a signal to sell or exit long positions, expecting a pullback.
Oversold (Blue Bars):
Blue bars (RSI below 30) suggest the market is oversold. Look for buying opportunities or consider exiting short positions, anticipating a rebound.
Neutral (Gray/Green Bars):
Gray or green bars (RSI near 50) indicate neutral conditions. You may want to wait for a clearer trend before taking action.
RSI is best used with other indicators to provide confirmations.
Tian Di Grid Merge Version 6.0
Strategy Introduction:
1. We know that the exchange can only set a maximum of 100 grids. However, our grid strategy can set a maximum of 350 grids.
2. We have added the modes of proportional and differential warehousing.
3. It should be noted that we have not set any filtering conditions, which means that when the price falls below the grid, we will execute a buy action at the closing price, and when the price falls above the grid, we will execute a sell action;
4. We suggest limiting the trading time cycle to 5 meters, as sometimes errors may appear on TV due to the dense grid or the inability to draw so many grids;
5. Please ensure that the minimum spacing between each grid is not less than 0.1%, as this is extremely difficult to profit from, and on the other hand, it may not function due to excessively dense spacing;
6. The maximum number of grids is 350, and the minimum number is currently 3;
matters needing attention:
Don't choose to go long or short together, and don't choose to go even short or short;
Closing position setting: It is recommended to select it to avoid order accumulation;
Unable to trade: If unable to trade normally, switch to a 1m cycle;
Number of cells: Calculate it yourself, 350 is just the maximum number of cells that can be adjusted;
Grid spacing: minimum 0.1%, below which no profit can be made;
Position value: default is 100u, which is the amount already leveraged;
Multiple investment: The order amount for each order is the same, and there is no need for multiple investment;
Open both long and short positions: You can open multiple positions for one account and open one position for one account. Do not open both long and short positions for the same target at the same time
Weighted Closing Price For Loop | viResearchWeighted Closing Price For Loop | viResearch
Conceptual Foundation and Innovation
The "Weighted Closing Price For Loop" indicator from viResearch offers a unique approach to trend analysis by incorporating a weighted average of the closing price into a loop-based scoring system. By giving more weight to the current closing price and less to previous ones, this method emphasizes recent market activity while smoothing out short-term fluctuations. This weighted approach allows traders to better assess the strength of ongoing trends. The For Loop component then evaluates the price movements over a specified range, assigning scores that help traders identify whether the market is in an uptrend or downtrend.
This combination of weighted closing price and loop-based evaluation provides a refined tool for tracking price momentum and assessing trend direction with greater precision.
Technical Composition and Calculation
The "Weighted Closing Price For Loop" script consists of two main components: the weighted closing price and the For Loop scoring system. The weighted closing price is calculated by applying a higher weight (90%) to the current closing price and a lower weight (10%) to the previous closing price, creating a smoothed average that reflects recent price action. The For Loop system iterates over a defined range of past values (determined by user input), comparing the weighted closing price to its previous values to generate a total score.
The loop evaluates whether the current weighted closing price is higher or lower than the previous ones within the range. A positive score indicates upward momentum, while a negative score suggests downward momentum. The score is then compared to user-defined thresholds to signal potential uptrends or downtrends, making it easier for traders to recognize shifts in market direction.
Features and User Inputs
The "Weighted Closing Price For Loop" script offers several customizable inputs, allowing traders to tailor the indicator to their trading strategies. The "From" and "To" inputs define the range over which the For Loop evaluates past price data, providing flexibility in assessing market trends over different time periods. Additionally, the Thresholds for uptrends and downtrends can be adjusted, enabling traders to fine-tune the sensitivity of the indicator. The script also includes color-coded visual cues and alert conditions to notify traders when the score crosses key threshold levels.
Practical Applications
The "Weighted Closing Price For Loop" indicator is designed for traders who want to track market trends with greater sensitivity to recent price movements. This tool is particularly effective for:
Detecting Trend Reversals: The loop-based scoring system evaluates the direction of the weighted closing price, providing early signals of potential trend reversals when the score crosses key thresholds. Improving Trade Timing: The weighted closing price focuses on recent market activity, allowing traders to refine their entry and exit points by responding to real-time price momentum. Assessing Trend Strength: The For Loop system compares recent price movements to historical data, giving traders a clearer understanding of whether the current trend is gaining or losing strength.
Advantages and Strategic Value
The "Weighted Closing Price For Loop" script offers significant value by combining the responsiveness of weighted closing prices with the analytical depth of a For Loop system. The weighted average ensures that the indicator is more attuned to recent market activity, while the loop-based evaluation provides a structured way to assess trend direction and strength. This dual approach helps traders identify trends earlier and with greater confidence, reducing the impact of short-term noise on their decision-making process. The ability to customize the evaluation range and thresholds further enhances the indicator’s adaptability to various market conditions.
Alerts and Visual Cues
The script includes alert conditions that notify traders when the score crosses key threshold levels, indicating potential uptrends or downtrends. The "Weighted Closing Price For Loop Long" alert is triggered when the score crosses above the upper threshold, signaling a potential upward trend. Conversely, the "Weighted Closing Price For Loop Short" alert is activated when the score drops below the lower threshold, suggesting a possible downward trend. Visual cues, such as color changes in the plot and background fill for trend zones, help traders quickly identify key moments of market movement.
Summary and Usage Tips
The "Weighted Closing Price For Loop | viResearch" indicator provides traders with a powerful tool for tracking trend direction and momentum. By incorporating this script into your trading strategy, you can improve your ability to detect trend reversals, confirm trend strength, and time your trades more effectively. The "Weighted Closing Price For Loop" offers a reliable and customizable solution for traders seeking to enhance their technical analysis with a focus on recent market activity and trend strength.
Note: Backtests are based on past results and are not indicative of future performance.
Advanced Stochastic ForLoopAdvanced Stochastic ForLoop
OVERVIEW
Advanced Stochastic ForLoop is an improved version of Stochastic it is designed to calculate an array of values 1 or -1 depending if soruce for calculations is above or below basis.
It takes avereage of values over a range of lengths, providing trend signals smothed based on various moving averages in order to get rid of noise.
It offers flexibility with different signal modes and visual customizations.
TYPE OF SIGNALS
-FAST (MA > MA or MA > 0.99)
-SLOW (MA > 0)
-THRESHOLD CROSSING (set by user treshold for both directions)
-FAST THRESHOLD (when theres an change in signal by set margin e.g 0.4 -> 0.2 means bearsih when FT is set to 0.1, when MA is > 0.99 it will signal bullish, when MA < -0.99 it will signal bearish)
Generaly Lime color of line indicates Bullish, Fuchsia indicates Bearish.
This colors are not set in stone so you can change them in settings.
Alerts included when line color is:
-Bullish Trend, line color is lime
-Bearish Trend, line color is fuchsia
Credit
Idea for this script was from one of indicators created by www.tradingview.com
Warning
This indicator can be really noisy depending on the settings, signal mode so it should be used preferably as a part of an strategy not as a stand alone indicator
Remember the lower the timeframe you use the more noise there is.
No single indicator should be used alone when making investment decisions.
Bollinger Bands with RSI Buy/Sell Signals (15 min) Bollinger Bands with RSI Buy/Sell Signals (15 Min)
Description:
The Bollinger Bands with RSI Buy/Sell Signals (15 Min) indicator is designed to help traders identify potential reversal points in the market using two popular technical indicators: Bollinger Bands and the Relative Strength Index (RSI).
How It Works:
Bollinger Bands:
Bollinger Bands consist of an upper band, lower band, and a middle line (Simple Moving Average). These bands adapt to market volatility, expanding during high volatility and contracting during low volatility.
This indicator monitors the 15-minute Bollinger Bands. If the price moves completely outside the bands, it signals that the market is potentially overextended.
Relative Strength Index (RSI):
RSI is a momentum indicator that measures the strength of price movements. RSI readings above 70 indicate an overbought condition, while readings below 30 suggest an oversold condition.
This indicator uses the RSI on the 15-minute time frame to further confirm overbought and oversold conditions.
Buy/Sell Signal Generation:
Buy Signal:
A buy signal is triggered when the market price crosses above the lower Bollinger Band on the 15-minute time frame, indicating that the market may be oversold.
Additionally, the RSI must be below 30, confirming an oversold condition.
A "Buy" label appears below the price when this condition is met.
Sell Signal:
A sell signal is triggered when the market price crosses below the upper Bollinger Band on the 15-minute time frame, indicating that the market may be overbought.
The RSI must be above 70, confirming an overbought condition.
A "Sell" label appears above the price when this condition is met.
Dynamic Jurik RSX w/ Fisher Transform█ Introduction
The Dynamic Jurik RSX with Fisher Transform is a powerful and adaptive momentum indicator designed for traders who seek a non-laggy view of price movements. This script is based on the classic Jurik RSX (Relative Strength Index). It also includes features such as the dynamic overbought and oversold limits, the Inverse Fisher Transform, trend display, slope calculations, and the ability to color extremes for better clarity.
█ Key Features:
• RSX: The Relative Strength Index (RSX) in this script is based on Jurik’s RSX, which is smoother than the traditional RSI and aims to reduce noise and lag. This script calculates the RSX using an exponential smoothing technique and adaptive adjustments.
• Inverse Fisher Transform: This script can optionally apply the Inverse Fisher Transform to the RSX, which helps to normalize the RSX values, compressing them between -1 and 1. The inverse transformation makes it easier to spot extreme values (overbought and oversold conditions) by enhancing the visual clarity of those extremes. It also smooths the curve over a user-defined period in hopes of providing a more consistent signal.
• Dynamic Limits: The dynamic overbought and oversold limits are calculated based on the RSX's recent high and low values. The limits adjust dynamically depending on market conditions, making them more relevant to current price action.
• Slope Display: The slope of the RSX is calculated as the rate of change between the current and previous RSX value. The slope is displayed as dots when the slope exceeds the threshold designated by the user, providing visual cues for momentum shifts.
• Trend Coloring: Optionally, the user can also enable a trend-based display. It is simply based on current value of RSX versus the previous one. If RSX is rising then the trend is bullish, if not, then the trend is bearish.
• Coloring Extremes: Users can configure the RSX to color the chart when prices enter extreme conditions, such as overbought or oversold zones, providing visual cues for market reversals.
█ Attached Chart Notes:
• Top Panel: Enabled dynamic limits, Trend display, standard Jurik RSX with 20 lookback period, and Slope display.
• Middle Panel: Enabled dynamic limits, Extremes display, and standard Jurik RSX with 20 lookback period.
• Bottom Panel: Enabled dynamic limits, Trend display, Inverse Fisher Transform with 14 lookback period and 9 smoothing period. and Slope display.
█ Credits:
Special thanks to Everget for providing the original script. The script was also slightly modified based on updates from outside sources.
█ Disclaimer:
This script is for educational purposes only and should not be considered financial advice. Always conduct your own research and consult a professional before making any trading decisions.
Adaptive RSI-Stoch with Butterworth Filter [UAlgo]The Adaptive RSI-Stoch with Butterworth Filter is a technical indicator designed to combine the strengths of the Relative Strength Index (RSI), Stochastic Oscillator, and a Butterworth Filter to provide a smooth and adaptive momentum-based trading signal. This custom-built indicator leverages the RSI to measure market momentum, applies Stochastic calculations for overbought/oversold conditions, and incorporates a Butterworth Filter to reduce noise and smooth out price movements for enhanced signal reliability.
By utilizing these combined methods, this indicator aims to help traders identify potential market reversal points, momentum shifts, and overbought/oversold conditions with greater precision, while minimizing false signals in volatile markets.
🔶 Key Features
Adaptive RSI and Stochastic Oscillator: Calculates RSI using a configurable period and applies a dual-smoothing mechanism with Stochastic Oscillator values (K and D lines).
Helps in identifying momentum strength and potential trend reversals.
Butterworth Filter: An advanced signal processing filter that reduces noise and smooths out the indicator values for better trend identification.
The filter can be enabled or disabled based on user preferences.
Customizable Parameters: Flexibility to adjust the length of RSI, the smoothing factors for Stochastic (K and D values), and the Butterworth Filter period.
🔶 Interpreting the Indicator
RSI & Stochastic Calculations:
The RSI is calculated based on the closing price over the user-defined period, and further smoothed to generate Stochastic Oscillator values.
The K and D values of the Stochastic Oscillator provide insights into short-term overbought or oversold conditions.
Butterworth Filter Application:
What is Butterworth Filter and How It Works?
The Butterworth Filter is a type of signal processing filter that is designed to have a maximally flat frequency response in the passband, meaning it doesn’t distort the frequency components of the signal within the desired range. It is widely used in digital signal processing and technical analysis to smooth noisy data while preserving the important trends in the underlying data. In this indicator, the Butterworth Filter is applied to the trigger value, making the resulting signal smoother and more stable by filtering out short-term fluctuations or noise in price data.
Key Concepts Behind the Butterworth Filter:
Filter Design: The Butterworth filter works by calculating weighted averages of current and past inputs (price or indicator values) and outputs to produce a smooth output. It is characterized by the absence of ripple in the passband and a smooth roll-off after the cutoff frequency.
Cutoff Frequency: The period specified in the indicator acts as a control for the cutoff frequency. A higher period means the filter will remove more high-frequency noise and retain longer-term trends, while a lower period means it will respond more to short-term fluctuations in the data.
Smoothing Process: In this script, the Butterworth Filter is calculated recursively using the following formula,
butterworth_filter(series float input, int period) =>
float wc = math.tan(math.pi / period)
float k1 = 1.414 * wc
float k2 = wc * wc
float a0 = k2 / (1 + k1 + k2)
float a1 = 2 * a0
float a2 = a0
float b1 = 2 * (k2 - 1) / (1 + k1 + k2)
float b2 = (1 - k1 + k2) / (1 + k1 + k2)
wc: This is the angular frequency, derived from the period input.
k1 and k2: These are intermediate coefficients used in the filter calculation.
a0, a1, a2: These are the feedforward coefficients, which determine how much of the current and past input values will contribute to the filtered output.
b1, b2: These are feedback coefficients, which determine how much of the past output values will contribute to the current output, effectively allowing the filter to "remember" past behavior and smooth the signal.
Recursive Calculation: The filter operates by taking into account not only the current input value but also the previous two input values and the previous two output values. This recursive nature helps it smooth the signal by blending the recent past data with the current data.
float filtered_value = a0 * input + a1 * prev_input1 + a2 * prev_input2
filtered_value -= b1 * prev_output1 + b2 * prev_output2
input: The current input value, which could be the trigger value in this case.
prev_input1, prev_input2: The previous two input values.
prev_output1, prev_output2: The previous two output values.
This means the current filtered value is determined by the combination of:
A weighted sum of the current input and the last two inputs.
A correction based on the last two output values to ensure smoothness and remove noise.
In conclusion when filter is enabled, the Butterworth Filter smooths the RSI and Stochastic values to reduce market noise and highlight significant momentum shifts.
The filtered trigger value (post-Butterworth) provides a cleaner representation of the market's momentum.
Cross Signals for Trade Entries:
Buy Signal: A bullish crossover of the K value above the D value, particularly when the values are below 40 and when the Stochastic trigger is below 1 and the filtered trigger is below 35.
Sell Signal: A bearish crossunder of the K value below the D value, particularly when the values are above 60 and when the Stochastic trigger is above 99 and the filtered trigger is above 90.
These signals are plotted visually on the chart for easy identification of potential trading opportunities.
Overbought and Oversold Zones:
The indicator highlights the overbought zone when the filtered trigger surpasses a specific threshold (typically above 100) and the oversold zone when it drops below 0.
The color-coded fill areas between the Stochastic and trigger lines help visualize when the market may be overbought (likely a reversal down) or oversold (potential reversal up).
🔶 Disclaimer
Use with Caution: This indicator is provided for educational and informational purposes only and should not be considered as financial advice. Users should exercise caution and perform their own analysis before making trading decisions based on the indicator's signals.
Not Financial Advice: The information provided by this indicator does not constitute financial advice, and the creator (UAlgo) shall not be held responsible for any trading losses incurred as a result of using this indicator.
Backtesting Recommended: Traders are encouraged to backtest the indicator thoroughly on historical data before using it in live trading to assess its performance and suitability for their trading strategies.
Risk Management: Trading involves inherent risks, and users should implement proper risk management strategies, including but not limited to stop-loss orders and position sizing, to mitigate potential losses.
No Guarantees: The accuracy and reliability of the indicator's signals cannot be guaranteed, as they are based on historical price data and past performance may not be indicative of future results.
Smoothed Wma Z-score | viResearchSmoothed Wma Z-score | viResearch
Conceptual Foundation and Innovation
The "Smoothed Wma Z-score" indicator from viResearch integrates the Weighted Moving Average (WMA) with Z-score analysis, providing traders with a precise tool for identifying market extremes and potential reversions. The WMA gives more weight to recent data, making it highly responsive to short-term price fluctuations, while the Z-score standardizes this price action relative to its historical mean and volatility. By smoothing the WMA and applying Z-score analysis, this indicator helps traders detect when the market is either overbought or oversold, offering actionable signals for mean reversion or trend continuation strategies.
The combination of WMA smoothing and Z-score analysis allows traders to better evaluate the strength of market trends while pinpointing moments when price may be stretched beyond its typical range.
Technical Composition and Calculation
The "Smoothed Wma Z-score" script consists of two primary components: the Weighted Moving Average (WMA) and the Z-score. The WMA is calculated using a user-defined period, applying more weight to recent price data to provide a smoothed representation of the price trend. The Z-score is then derived by measuring how far the current WMA deviates from its historical mean, normalized by its standard deviation over a specified lookback period. This calculation gives a standardized measure of price extremes, allowing traders to determine whether the current price is statistically far from its norm.
The script compares the Z-score with customizable threshold levels to generate buy and sell signals. A Z-score exceeding the upper threshold suggests potential overbought conditions, while a Z-score below the lower threshold may indicate oversold conditions. Additionally, the script highlights areas where price is in the "mean reversion zone," helping traders anticipate when price might revert back to its average.
Features and User Inputs
The "Smoothed Wma Z-score" script offers several customizable inputs, enabling traders to tailor the indicator to their specific trading strategies. The WMA Length determines the sensitivity of the WMA to price changes, while the Lookback Period controls the range over which the mean and standard deviation of the WMA are calculated for the Z-score. Traders can also adjust the thresholds to define the sensitivity of overbought and oversold conditions. Furthermore, the script includes alert conditions that notify traders when trend shifts occur, allowing for timely responses to market movements.
Practical Applications
The "Smoothed Wma Z-score" indicator is designed for traders who focus on identifying price extremes and potential mean reversion opportunities. By combining WMA smoothing with Z-score analysis, this tool can be particularly effective for detecting points of overextension in the market, where a reversion to the mean is likely. The indicator is valuable for traders who seek to capitalize on:
Detecting Overbought and Oversold Conditions: The Z-score measures how far the price has deviated from its norm, allowing traders to identify overbought or oversold conditions with precision. Timing Market Reversals: The indicator provides early signals of potential market reversals by highlighting when the price has moved too far away from its average, helping traders anticipate reversion opportunities. Improving Trend Continuation Strategies: The WMA’s responsiveness to recent price changes, combined with the Z-score’s ability to measure deviations, offers traders a clearer understanding of whether a trend is likely to continue or if it’s overextended.
Advantages and Strategic Value
The "Smoothed Wma Z-score" script provides significant value by integrating WMA smoothing with Z-score analysis, delivering a powerful combination for traders seeking to identify extreme price movements. The ability to smooth price data while detecting statistically significant deviations ensures that traders are better equipped to spot reversals or continuation signals. This dual approach helps reduce noise in price data while offering a robust method for timing entries and exits, making the "Smoothed Wma Z-score" a versatile tool for both mean reversion and trend-following strategies.
Alerts and Visual Cues
The script includes alert conditions that notify traders when key thresholds are crossed. The "Smoothed Wma Z-score Long" alert is triggered when the Z-score moves above the upper threshold, signaling potential overbought conditions. The "Smoothed Wma Z-score Short" alert is activated when the Z-score drops below the lower threshold, indicating possible oversold conditions. Visual cues, such as color changes in the Z-score plot and highlighted mean reversion zones, help traders quickly identify critical market conditions and make timely decisions.
Summary and Usage Tips
The "Smoothed Wma Z-score | viResearch" indicator provides traders with a powerful tool for analyzing price extremes and identifying mean reversion opportunities. By incorporating this script into your trading strategy, you can improve your ability to spot overbought and oversold conditions, timing market reversals with greater accuracy. The "Smoothed Wma Z-score" is a reliable and customizable solution for traders focused on both mean reversion and trend-following strategies in volatile market environments.
Note: Backtests are based on past results and are not indicative of future performance.
Dema EFI Volume | viResearchDema EFI Volume | viResearch
Conceptual Foundation and Innovation
The "Dema EFI Volume" indicator from viResearch integrates the Double Exponential Moving Average (DEMA) with the Elder Force Index (EFI), providing a dynamic approach to analyzing both price trends and volume strength. The DEMA is applied to smooth out price fluctuations while minimizing lag, which enhances the ability to detect trend direction. The EFI, developed by Dr. Alexander Elder, measures the power behind price movements by incorporating both price change and volume. This indicator, when combined with DEMA smoothing, gives traders a more accurate understanding of whether the current price movements are supported by significant volume, helping them make more informed trading decisions. The combination of DEMA and EFI allows traders to track trend strength while assessing the market’s volume dynamics, offering a more reliable method for identifying potential trend continuations or reversals.
Technical Composition and Calculation
The "Dema EFI Volume" script consists of two key components: the Double Exponential Moving Average (DEMA) and the Elder Force Index (EFI). The DEMA is applied to the selected source price over a user-defined length, providing a smoothed representation of price movements while reducing the noise that can occur with traditional moving averages. The EFI is calculated by multiplying the change in the DEMA by the volume over a user-defined period, which indicates whether the price movement is being driven by strong or weak volume. The script monitors the EFI values and volume data to generate trend signals. If the EFI is positive and volume increases, this indicates bullish pressure, while a negative EFI with decreasing volume suggests bearish conditions. The combination of these signals helps traders determine whether a price move is backed by sufficient volume, making it easier to identify trend continuations or potential reversals.
Features and User Inputs
The "Dema EFI Volume" script offers several customizable inputs, allowing traders to adapt the indicator to their specific strategies. The DEMA Length controls the smoothing applied to the price data, while the EFI Length defines the period over which the force index is calculated. Additionally, traders can set alert conditions for when a bullish or bearish EFI signal occurs, enabling them to react quickly to changing market conditions.
Practical Applications
The "Dema EFI Volume" indicator is designed for traders who want to combine price trend analysis with volume dynamics in a single tool. This makes it particularly effective for identifying trend continuations, as rising volume alongside a positive EFI suggests that the market move is supported by strong momentum. Conversely, decreasing volume and a negative EFI may indicate a weakening trend, giving traders early warning of potential reversals. The combination of DEMA and EFI also makes this indicator valuable for detecting trend strength by measuring whether price movements are backed by strong volume, confirming trend reversals by comparing price changes with volume activity, and improving trade entries and exits by analyzing both price and volume for more robust signals.
Advantages and Strategic Value
The "Dema EFI Volume" script offers significant advantages by combining the DEMA’s smoothing power with the EFI’s volume analysis. This integration allows traders to filter out noise in price data while ensuring that trend signals are backed by meaningful volume. The result is a more reliable tool for trend-following and reversal detection, making it easier for traders to stay aligned with strong market moves while avoiding false signals caused by low-volume fluctuations. The dual focus on price and volume makes the "Dema EFI Volume" an ideal tool for traders who value a comprehensive approach to market analysis.
Alerts and Visual Cues
The script includes alert conditions that notify traders when a significant EFI signal occurs. The "EFI Volume Long" alert is triggered when the EFI is positive and volume increases, indicating a potential upward trend. The "EFI Volume Short" alert signals a possible downward trend when the EFI turns negative and volume decreases. Visual cues, such as the color and direction of the plotted EFI line, help traders quickly identify trend shifts and make timely decisions.
Summary and Usage Tips
The "Dema EFI Volume | viResearch" indicator provides traders with a powerful tool for analyzing both price trends and volume strength. By incorporating this script into your trading strategy, you can improve your ability to detect trend continuations and reversals, making more informed decisions based on a combination of price movement and volume dynamics. Whether you are focused on identifying trend strength or looking for early reversal signals, the "Dema EFI Volume" offers a reliable and customizable solution for traders of all levels.
Note: Backtests are based on past results and are not indicative of future performance.
RSI TreeRSI Tree is a simple way to compare the strength of several different instruments against each other.
The default is to compare MSFT, NVDA, TSLA, GOOG, META, AMZN, AAPL and NASDAQ. You could do the same for currency pairs and any other instruments available in Trading View. However, it makes the most sense to compare seven instruments to an eighth underlying instrument. As you can see in the default values, we included the NASDAQ as the eighth instrument since the other seven are part of the NASDAQ composite index. If you were to trade major currency pairs, then your eighth instrument would most likely be the U.S. Dollar (DXY).
The chart setup is important as well. You need to split your chart horizontally into 4 plots. Each plot would be at a different timing interval. The example shows 4 hr, 1 hr, 15 min and 5 min (left to right) charts. Now not only can we compare the instruments against each other, but we can do it across time to get an idea of the motion of each instrument.
Note, the instrument used on the chart is somewhat important. If the chart is set to a currency pair, but you have the RSI Tree setup for equities (as in the default) then you will get some odd behavior due to the times when these are open. Equities are 0930 to 1600 EST, whereas something like a currency would be open 24 hours a day.
Layout for default settings: www.tradingview.com
Bugs?
Kindly report any issues and I'll try to fix them promptly.
Thank you!
DCA, Support and Resistance with RSI and Trend FilterThis script is based on
script from Kieranj with added pyramiding and DCA
The buy condition (buyCondition) is triggered when the RSI crosses above the oversold threshold (ta.crossover(rsi, oversoldThreshold)), the trend filter confirms an uptrend (isUptrend is true), and the close price is greater than or equal to the support level (close >= supportLevel).
The partial sell condition (sellCondition) is triggered when the RSI crosses below the overbought threshold (ta.crossunder(rsi, overboughtThreshold)) and profit goal is reached, the trend filter confirms a downtrend (isUptrend is false), and the close price is less than or equal to the resistance level (close <= resistanceLevel).
Full sell will be triggered if trend is broken and profit goal is reached
With this implementation, the signals will only be generated in the direction of the trend on the 4-hour timeframe. The trend is considered up when the 50-period SMA is below the 200-period SMA (ta.sma(trendFilterSource, 50) < ta.sma(trendFilterSource, 200)).
Pyramiding should be activated, values like 100, so every DCA step should be around 1%
i have best results on 5 min charts
DILM TRADING - Market Sentiment and FibonacciDILM TRADING - Market Sentiment and Fibonacci
Overview
The DILM TRADING - Market Sentiment and Fibonacci indicator is designed to provide traders with a comprehensive view of market trends and potential trading opportunities. By combining several popular technical indicators such as the SuperTrend, Fibonacci levels, and multiple sentiment indicators, this tool offers a deep analysis of market dynamics. Each component has been carefully selected to work in harmony, providing users with reliable entry and exit signals and helping them navigate volatile markets.
Why This Combination?
This indicator brings together different elements with specific purposes:
SuperTrend: A trend-following indicator that helps identify the market's current direction and acts as a dynamic stop-loss tool.
Fibonacci Levels: Known for pinpointing potential market reversal points, these levels provide crucial support and resistance areas for traders to set stop-losses and take-profits.
Sentiment Indicators: Tools like RSI, MACD, and Ichimoku are combined to gauge market momentum, allowing traders to assess whether a market is overbought or oversold, and whether the current trend is strong enough to continue or reverse.
The combination of these indicators gives traders a complete framework for analyzing the market: trend direction, market sentiment, and key price levels. Each of these elements works in tandem to provide signals that are both timely and accurate.
Key Features
SuperTrend
Based on the Average True Range (ATR), the SuperTrend indicator is an excellent way to determine the current trend. If the price is above the SuperTrend line, it suggests an uptrend, whereas if the price is below it, a downtrend is indicated. It is also a highly effective tool for setting trailing stop-losses, thereby improving risk management.
Fibonacci Levels
The script automatically calculates Fibonacci retracement levels based on the highest and lowest points within a specific timeframe. These levels are essential for identifying potential reversal zones, key areas for stop-losses, and take-profit levels. The levels adjust according to the prevailing trend, making them a dynamic and responsive tool for any market condition.
Sentiment Indicators
This section integrates multiple sentiment indicators to give a holistic view of market direction:
Ichimoku Cloud: Measures the strength of trends and identifies potential reversal zones using clouds (Kumo).
OBV (On-Balance Volume): Tracks volume changes to confirm the direction of price movements.
CMF (Chaikin Money Flow): Monitors the money flow to identify buying or selling pressure.
RSI (Relative Strength Index): Highlights overbought or oversold conditions, signaling potential trend reversals.
MACD: A reliable tool for identifying bullish and bearish crossovers.
ADX (Average Directional Index): Determines the strength of the prevailing trend, helping to confirm whether it's likely to continue or weaken.
Volatility Filter
The ATR (Average True Range) acts as a filter to identify periods of high or low volatility, helping traders to adapt their strategies to the current market environment. High volatility suggests larger price swings, potentially offering better trading opportunities, while low volatility indicates consolidation or range-bound conditions.
Order Blocks
The script visually identifies bullish and bearish order blocks on the chart. These zones represent areas where significant buying or selling occurred, making them crucial for spotting potential breakout or reversal points.
How to Use
Entry/Exit: Fibonacci levels (50% or 61.8%) serve as potential entry points, while the 0% and 100% levels can be used to set take-profit and stop-loss levels.
Sentiment Analysis: The overall market sentiment is derived from the combination of Ichimoku, OBV, CMF, RSI, ADX, and other tools, helping traders make informed decisions on whether to buy or sell.
Risk Management: Use SuperTrend and Fibonacci levels to set precise stop-loss points and improve risk management.
New Feature: Moving Average and RSI Confirmation
A recent addition allows users to calculate two moving averages (short and long) and the RSI on a timeframe of their choice. An entry signal is generated when the short moving average crosses above the long, and the RSI is below a specific threshold. Conversely, a sell signal is displayed when the short moving average crosses below the long, and the RSI is above a defined level.
Limitations
This indicator may be less effective during periods of low volatility or range-bound markets. It's important to use this tool in conjunction with other analysis techniques, as relying on a single indicator could lead to false signals.
DILM TRADING - Sentiment de marché et Fibonacci
Vue d'ensemble
L'indicateur DILM TRADING - Sentiment de marché et Fibonacci a été conçu pour offrir une vue d'ensemble des tendances du marché et des opportunités de trading potentielles. En combinant plusieurs indicateurs techniques populaires, tels que le SuperTrend, les niveaux de Fibonacci, et divers indicateurs de sentiment, cet outil fournit une analyse complète des dynamiques du marché. Chaque composant a été soigneusement sélectionné pour fonctionner ensemble, offrant des signaux d'entrée et de sortie fiables.
Pourquoi cette combinaison ?
Cette combinaison d'indicateurs permet de fournir un cadre complet pour analyser le marché. Le SuperTrend permet d'identifier la tendance, tandis que les niveaux de Fibonacci aident à déterminer les zones de retournement clés. Les indicateurs de sentiment, comme le RSI et le MACD, ajoutent une dimension supplémentaire en mesurant la force et la direction du marché.
Caractéristiques clés et Utilisation
SuperTrend : Indique la tendance actuelle et propose des niveaux de stop-loss dynamiques.
Niveaux de Fibonacci : Utilisés pour repérer des points de retournement potentiels et définir des niveaux de stop-loss et de take-profit.
Indicateurs de Sentiment : Outils comme l'Ichimoku, le RSI, et l'ADX fournissent une analyse globale du marché, permettant de prendre des décisions éclairées.
Nouvelle fonctionnalité : Confirmation des Moyennes Mobiles et RSI
Cette fonctionnalité permet d'utiliser deux moyennes mobiles et le RSI pour générer des signaux d'achat et de vente basés sur les croisements et les niveaux de surachat/survente du RSI.
Conclusion
Le DILM TRADING - Sentiment de marché et Fibonacci est un outil puissant et polyvalent, conçu pour les traders cherchant à affiner leurs stratégies grâce à une analyse complète des tendances et du sentiment du marché.
Bitcoin Thermocap [InvestorUnknown]The Bitcoin Thermocap indicator is designed to analyze Bitcoin's market data using a variant of the "Thermocap Multiple" concept from BitBo. This indicator offers several modes for interpreting Bitcoin's historical block and price data, aiding investors and analysts in understanding long-term market dynamics and generating potential investing signals.
Key Features:
1. Thermocap Calculation
The core of the indicator is based on the Thermocap Multiple, which evaluates Bitcoin's value relative to its cumulative historical blocks mined.
Thermocap Formula:
Source: Bitbo
btc_price = request.security("INDEX:BTCUSD", "1D", close)
BTC_BLOCKSMINED = request.security("BTC_BLOCKSMINED", "D", close)
// Variable to store the cumulative historical blocks
var float historical_blocks = na
// Initialize historical blocks on the first bar
if (na(historical_blocks))
historical_blocks := 0.0
// Update the cumulative blocks for each day
historical_blocks += BTC_BLOCKSMINED * btc_price
// Calculate the Thermocap
float thermocap = ((btc_price / historical_blocks) * 1000000) // the multiplication is just for better visualization
2. Multiple Display Modes:
The indicator can display data in four different modes, offering flexibility in interpretation:
RAW: Displays the raw Thermocap value.
LOG: Applies the logarithm of the Thermocap to visualize long-term trends more effectively, especially for large-value fluctuations.
MA Oscillator: Shows the ratio between the Thermocap and its moving average (MA). Users can choose between Simple Moving Average (SMA) or Exponential Moving Average (EMA) for smoothing.
Normalized MA Oscillator: Provides a normalized version of the MA Oscillator using a dynamic min-max rescaling technique.
3. Normalization and Rescaling
The indicator normalizes the Thermocap Oscillator values between user-defined limits, allowing for easier interpretation. The normalization process decays over time, with values shrinking towards zero, providing more relevance to recent data.
Negative values can be allowed or restricted based on user preferences.
f_rescale(float value, float min, float max, float limit, bool negatives) =>
((limit * (negatives ? 2 : 1)) * (value - min) / (max - min)) - (negatives ? limit : 0)
f_max_min_normalized_oscillator(float x) =>
float oscillator = x
var float min = na
var float max = na
if (oscillator > max or na(max)) and time >= normalization_start_date
max := oscillator
if (min > oscillator or na(min)) and time >= normalization_start_date
min := oscillator
if time >= normalization_start_date
max := max * decay
min := min * decay
normalized_oscillator = f_rescale(x, min, max, lim, neg)
Usage
The Bitcoin Thermocap indicator is ideal for long-term market analysis, particularly for investors seeking to assess Bitcoin's relative value based on mining activity and price dynamics. The different display modes and customization options make it versatile for a variety of market conditions, helping users to:
Identify periods of overvaluation or undervaluation.
Generate potential buy/sell signals based on the MA Oscillator and its normalized version.
By leveraging this Thermocap-based analysis, users can gain a deeper understanding of Bitcoin's historical and current market position, helping to inform investment strategies.
Fetch Stoch RSI Swing Buy and SellThe "Fetch Stoch RSI Swing Buy and Sell" script is a custom trading indicator built in Pine Script for TradingView. It leverages the Stochastic RSI to generate both buy and sell signals based on user-defined thresholds for overbought and oversold conditions.
Key Features:
1: Stochastic RSI Calculation:
- The script uses the Stochastic RSI indicator, a momentum oscillator that measures the relative strength index (RSI) against its high-low range over a specific period.
- The %K and %D lines of the Stochastic RSI are smoothed using moving averages to help refine the signals.
2: Oversold Buy Signals:
- A buy signal is triggered when both the Stochastic RSI %K and %D values drop below a user-defined oversold level (default 20).
- The script tracks the number of dips below the oversold threshold and fires a buy signal after a specified number of dips (default 10).
- When the buy condition is met, a green upward triangle is plotted below the candle, and an alert is triggered.
3: Overbought Sell Signals:
- A sell signal is generated when the Stochastic RSI %K and %D exceed a user-defined overbought level (default 80).
- Similar to the buy condition, the script counts the number of tops above the overbought level and triggers a sell signal after a specific number of tops (default 10).
When the sell condition is met, a red downward triangle is plotted above the candle, and an alert is triggered.
This script is particularly useful for swing traders looking to capitalize on short-term reversals in the market, as it helps to identify potential entry and exit points based on momentum shifts.
Stoch RSI Time StatisticsThe “Stochastic RSI Time Statistics” is a comprehensive tool designed to enhance your trading decisions by combining the traditional Stochastic RSI with additional metrics and visual aids. This indicator can be used to detect overbought and oversold conditions, issue long and short alerts based on crossovers, and help you analyse market movements by providing detailed statistical insights.
The Stochastic RSI is an open source script that was developed by Tushar Chande and Stanley Kroll and introduced in their book "The New Technical Trader" in 1994. It combines two popular indicators: the “Relative Strength Index (RSI)” and the “Stochastic Oscillator”.
The “Stochastic RSI Time Statistics” uses the stochastic RSI calculations and additionally calculates various probability and frequency statistics to better understand the momentum oscillator’s behaviour and guide our strategies and risk management.
Statistics & Probabilities:
The indicator calculates important time and frequency-based metrics that provide deeper insight into the behaviour of the Stochastic RSI. These are displayed in a text box on the indicator panel, including:
Avg Long: The average number of bars between the last long signal before exiting the critical zone and the next short signal in the overbought critical zone, including the standard deviation and the sample size within the relevant time frame.
Avg Short: The average number of bars between the last short signal before exiting the critical zone and the next long signal in the oversold critical zone, including the standard deviation and the sample size within the relevant time frame.
Avg Consecutive Longs: The average number of consecutive long signals before the first proceeding short signal occurs, with standard deviation.
Avg Consecutive Shorts: The average number of consecutive short signals before the first proceeding long signal occurs, with standard deviation.
Time in Oversold: The average time (in bars/candle sticks) that the Stochastic RSI lines (K & D Lines both in critical zone) spends in the oversold region (below the buy signal level) after entering the oversold region and until both K & D lines depart from the oversold region, along with the standard deviation.
Time in Overbought: The average time (in bars/candle sticks) that the Stochastic RSI lines (K & D Lines both in critical zone) spends in the overbought region (above the sell signal level), after entering the overbought region and until both K & D lines depart from the overbought region, along with the standard deviation.
Signal Frequency: It calculates the percentage of a single, double, triple, and more than triple long or short signals that occur consecutively within the critical zone before the opposing signal occurs (e.g., 1Long: 40.54%, 2 Long:28.55%, 3Long 17.4%, >3 Long:13.51%, 1Short:36.15%, 2Short:30.41%, 3Short:17.57%, >3Short:15.88%).
Key Features:
Oversold: When the Stochastic RSI is below 20, it indicates that the RSI is in a low range, and the asset may be oversold, potentially signalling a buying opportunity.
Overbought: When the Stochastic RSI is above 80, it suggests the RSI is in a high range, meaning the asset may be overbought and a downturn might be near.
The Stochastic RSI Slope indicates the prominent trend direction within a relevant time period.
Customizable Buy Signal Level (typically below 20-25 percentile) to detect oversold conditions. Customizable Sell Signal Level (typically above 75-80 percentile) to detect overbought conditions. These levels help you spot potential reversal zones where long or short trades might be initiated.
Crossover Alerts:
The indicator tracks crossovers between the K and D lines, generating long signals when a crossover occurs below the buy signal level (indicating oversold conditions) and short signals when a cross under occurs above the sell signal level (indicating overbought conditions). The signals are visualized as labels on the chart:
**L** for potential long (buy) signals: Marked below the bars when the K line crosses above the D line.
**S** for potential short (sell) signals: Marked above the bars when the K line crosses below the D line.
Visual Alerts are generated based on these signals.
Risk Management
Although the Stochastic RSI is typically regarded as presenting trend direction and overbought and oversold conditions when in the extreme zones, the RSI can linger and cross over or under numerous times while in the critical zone. The statistics added to the Stochastic RSI indicator allows one to assess the statistical probability of numerous crossover signals occurring on an asset or at various time frames. Signal levels, or preferred definitions of the critical zones can be adjusted while the statistics are automatically updated to the relevant ticker or time frame. Colours and Signal shapes are adjustable to suite your visual preferences.
By using this indicator, you acknowledge and agree that:
No Guarantees: The indicator is provided "as-is" without any warranties or guarantees of accuracy, completeness, or fitness for a particular purpose. The outcomes or performance of trades executed using this indicator are not guaranteed to be successful or profitable.
User Responsibility: You are solely responsible for any trading decisions you make based on the use of this indicator. All trading and investment activities involve risk, and it is essential to conduct your own research, analysis, and due diligence before making any financial decisions.
No Liability: The creator of this indicator is not responsible for any financial losses, direct or indirect, incurred as a result of using this indicator. This includes, but is not limited to, loss of profits, loss of capital, or any other negative financial outcomes.
Market Risks: Markets are volatile, and prices may fluctuate significantly. Trading and investing carry inherent risks, and there is always the potential for loss. You should only trade with capital that you can afford to lose.
Independent Advice: It is strongly recommended that you seek independent financial advice from a qualified and licensed professional before making any trading or investment decisions based on the use of this indicator.
By using this indicator, you acknowledge that you fully understand and accept the risks involved, and you agree to indemnify and hold harmless the creator of this indicator from any claims, damages, or liabilities arising from its use.
The author of this script has made every effort to ensure that the code is an original interpretation and application of the open-source Stochastic RSI, as developed by the original authors, Tushar Chande and Stanley Kroll. The script reflects a unique adaptation aimed at enhancing trading strategies through advanced statistical analysis and trade management features. The author does not claim any proprietary rights over the foundational concepts of the Stochastic RSI and does not intend to infringe upon any existing copyrights. Should any copyright infringement be identified, the author commits to removing the indicator immediately and forfeits any rights to further or intended financial gain from its use.
RSItrendsThis is to my friends and to my sons to use.
What Is the Relative Strength Index (RSI)?
The relative strength index (RSI) is a momentum indicator used in technical analysis. RSI measures the speed and magnitude of a security's recent price changes to evaluate overvalued or undervalued conditions in the price of that security.
The RSI is displayed as an oscillator (a line graph) on a scale of zero to 100. The indicator was developed by J. Welles Wilder Jr. and introduced in his seminal 1978 book, New Concepts in Technical Trading Systems.
1
The RSI can do more than point to overbought and oversold securities. It can also indicate securities that may be primed for a trend reversal or corrective pullback in price. It can signal when to buy and sell. Traditionally, an RSI reading of 70 or above indicates an overbought situation. A reading of 30 or below indicates an oversold condition.
Adaptive LSMA Regression OscillatorOverview:
The Adaptive LSMA Regression Oscillator is an open-source technical analysis tool designed to reflect market price deviations from an adaptive least squares moving average (LSMA). The adaptive length of the LSMA changes dynamically based on the volatility of the market, making the indicator responsive to different market conditions.
Key Features:
Adaptive Length Adjustment : The base length of the LSMA is adjusted based on market volatility, measured by the Average True Range (ATR). The more volatile the market, the longer the adaptive length, and vice versa.
Oscillator : The indicator calculates the difference between the closing price and the adaptive LSMA. This difference is plotted as a histogram, showing whether prices are above or below the LSMA.
Color-Coded Histogram:
Positive values (where price is above the LSMA) are colored green.
Negative values (where price is below the LSMA) are colored red.
Debugging Information: The adaptive length is plotted for transparency, allowing users to see how the length changes based on the multiplier and ATR.
How It Works:
Inputs:
Base Length : This defines the starting length of the LSMA. It is adjusted based on market conditions.
Multiplier : A customizable multiplier is used to control how much the adaptive length responds to changes in volatility.
ATR Period : This determines the lookback period for the Average True Range calculation, a measure of market volatility.
Dynamic Adjustment:
The length of the LSMA is dynamically adjusted by multiplying the base length by a factor derived from ATR and the average close price.
This helps the indicator adapt to different market conditions, staying shorter during low volatility and longer during high volatility.
Example Use Cases:
Trend Analysis: By observing the oscillator, traders can see when prices deviate from a dynamically adjusted LSMA. This can be used to evaluate potential trend direction or changes in market behavior.
Volatility-Responsive Indicator: The adaptive length ensures that the indicator responds appropriately in both high and low volatility environments.
Larry Connors %b Strategy (Bollinger Band)Larry Connors’ %b Strategy is a mean-reversion trading approach that uses Bollinger Bands to identify buy and sell signals based on the %b indicator. This strategy was developed by Larry Connors, a renowned trader and author known for his systematic, data-driven trading methods, particularly those focusing on short-term mean reversion.
The %b indicator measures the position of the current price relative to the Bollinger Bands, which are volatility bands placed above and below a moving average. The strategy specifically targets times when prices are oversold within a long-term uptrend and aims to capture rebounds by buying at relatively low points and selling at relatively high points.
Strategy Rules
The basic rules of the %b Strategy are:
1. Trend Confirmation: The closing price must be above the 200-day moving average. This filter ensures that trades are made in alignment with a longer-term uptrend, thereby avoiding trades against the primary market trend.
2. Oversold Conditions: The %b indicator must be below 0.2 for three consecutive days. The %b value below 0.2 indicates that the price is near the lower Bollinger Band, suggesting an oversold condition.
3. Entry Signal: Enter a long position at the close when conditions 1 and 2 are met.
4. Exit Signal: Exit the position when the %b value closes above 0.8, signaling an overbought condition where the price is near the upper Bollinger Band.
How the Strategy Works
This strategy operates on the premise of mean reversion, which suggests that extreme price movements will revert to the mean over time. By entering positions when the %b value indicates an oversold condition (below 0.2) in a confirmed uptrend, the strategy attempts to capture short-term price rebounds. The exit rule (when %b is above 0.8) aims to lock in profits once the price reaches an overbought condition, often near the upper Bollinger Band.
Who Was Larry Connors?
Larry Connors is a well-known figure in the world of financial markets and trading. He co-authored several influential trading books, including “Short-Term Trading Strategies That Work” and “High Probability ETF Trading.” Connors is recognized for his quantitative approach, focusing on systematic, rules-based strategies that leverage historical data to validate trading edges.
His work primarily revolves around short-term trading strategies, often using technical indicators like RSI (Relative Strength Index), Bollinger Bands, and moving averages. Connors’ methodologies have been widely adopted by traders seeking structured approaches to exploit short-term inefficiencies in the market.
Risks of the Strategy
While the %b Strategy can be effective, particularly in mean-reverting markets, it is not without risks:
1. Mean Reversion Assumption: The strategy is based on the assumption that prices will revert to the mean. In trending or sharply falling markets, this reversion may not occur, leading to sustained losses.
2. False Signals in Choppy Markets: In volatile or sideways markets, the strategy may generate multiple false signals, resulting in whipsaw trades that can erode capital through frequent small losses.
3. No Stop Loss: The basic implementation of the strategy does not include a stop loss, which increases the risk of holding losing trades longer than intended, especially if the market continues to move against the position.
4. Performance During Market Crashes: During major market downturns, the strategy’s buy signals could be triggered frequently as prices decline, compounding losses without the presence of a risk management mechanism.
Scientific References and Theoretical Basis
The %b Strategy relies on the concept of mean reversion, which has been extensively studied in finance literature. Studies by Avellaneda and Lee (2010) and Bouchaud et al. (2018) have demonstrated that mean-reverting strategies can be profitable in specific market environments, particularly when combined with volatility filters like Bollinger Bands. However, the same studies caution that such strategies are highly sensitive to market conditions and often perform poorly during periods of prolonged trends.
Bollinger Bands themselves were popularized by John Bollinger and are widely used to assess price volatility and detect potential overbought and oversold conditions. The %b value is a critical part of this analysis, as it standardizes the position of price relative to the bands, making it easier to compare conditions across different securities and time frames.
Conclusion
Larry Connors’ %b Strategy is a well-known mean-reversion technique that leverages Bollinger Bands to identify buying opportunities in uptrending markets when prices are temporarily oversold. While the strategy can be effective under the right conditions, traders should be aware of its limitations and risks, particularly in trending or highly volatile markets. Incorporating risk management techniques, such as stop losses, could help mitigate some of these risks, making the strategy more robust against adverse market conditions.
Larry Connors RSI 3 StrategyThe Larry Connors RSI 3 Strategy is a short-term mean-reversion trading strategy. It combines a moving average filter and a modified version of the Relative Strength Index (RSI) to identify potential buying opportunities in an uptrend. The strategy assumes that a short-term pullback within a long-term uptrend is an opportunity to buy at a discount before the trend resumes.
Components of the Strategy:
200-Day Simple Moving Average (SMA): The price must be above the 200-day SMA, indicating a long-term uptrend.
2-Period RSI: This is a very short-term RSI, used to measure the speed and magnitude of recent price changes. The standard RSI is typically calculated over 14 periods, but Connors uses just 2 periods to capture extreme overbought and oversold conditions.
Three-Day RSI Drop: The RSI must decline for three consecutive days, with the first drop occurring from an RSI reading above 60.
RSI Below 10: After the three-day drop, the RSI must reach a level below 10, indicating a highly oversold condition.
Buy Condition: All the above conditions must be satisfied to trigger a buy order.
Sell Condition: The strategy closes the position when the RSI rises above 70, signaling that the asset is overbought.
Who Was Larry Connors?
Larry Connors is a trader, author, and founder of Connors Research, a firm specializing in quantitative trading research. He is best known for developing strategies that focus on short-term market movements. Connors co-authored several popular books, including "Street Smarts: High Probability Short-Term Trading Strategies" with Linda Raschke, which has become a staple among traders seeking reliable, rule-based strategies. His research often emphasizes simplicity and robust testing, which appeals to both retail and institutional traders.
Scientific Foundations
The Relative Strength Index (RSI), originally developed by J. Welles Wilder in 1978, is a momentum oscillator that measures the speed and change of price movements. It oscillates between 0 and 100 and is typically used to identify overbought or oversold conditions in an asset. However, the use of a 2-period RSI in Connors' strategy is unconventional, as most traders rely on longer periods, such as 14. Connors' research showed that using a shorter period like 2 can better capture short-term reversals, particularly when combined with a longer-term trend filter such as the 200-day SMA.
Connors' strategies, including this one, are built on empirical research using historical data. For example, in a study of over 1,000 signals generated by this strategy, Connors found that it performed consistently well across various markets, especially when trading ETFs and large-cap stocks (Connors & Alvarez, 2009).
Risks and Considerations
While the Larry Connors RSI 3 Strategy is backed by empirical research, it is not without risks:
Mean-Reversion Assumption: The strategy is based on the premise that markets revert to the mean. However, in strong trending markets, the strategy may underperform as prices can remain oversold or overbought for extended periods.
Short-Term Nature: The strategy focuses on very short-term movements, which can result in frequent trading. High trading frequency can lead to increased transaction costs, which may erode profits.
Market Conditions: The strategy performs best in certain market environments, particularly in stable uptrends. In highly volatile or strongly trending markets, the strategy's performance can deteriorate.
Data and Backtesting Limitations: While backtests may show positive results, they rely on historical data and do not account for future market conditions, slippage, or liquidity issues.
Scientific literature suggests that while technical analysis strategies like this can be effective in certain market conditions, they are not foolproof. According to Lo et al. (2000), technical strategies may show patterns that are statistically significant, but these patterns often diminish once they are widely adopted by traders.
References
Connors, L., & Alvarez, C. (2009). Short-Term Trading Strategies That Work. TradingMarkets Publishing Group.
Lo, A. W., Mamaysky, H., & Wang, J. (2000). Foundations of Technical Analysis: Computational Algorithms, Statistical Inference, and Empirical Implementation. The Journal of Finance, 55(4), 1705-1770.
Wilder, J. W. (1978). New Concepts in Technical Trading Systems. Trend Research
Potential Divergence Checker#### Key Features
1. Potential Divergence Signals:
Potential divergences can signal a change in price movement before it occurs. This indicator identifies potential divergences instead of waiting for full confirmation, allowing it to detect signs of divergence earlier than traditional methods. This provides more flexible entry points and can act as a broader filter for potential setups.
2. Exposing Signals for External Use:
One of its advanced features is the ability to expose signals for use in other scripts. This allows users to integrate divergence signals and related entry/exit points into custom strategies or automated systems.
3. Custom Entry/Exit Timing Based on Years and Days:
The indicator provides entry and exit signals based on years and days, which could be useful for time-specific market behavior, long-term trades, and back testing.
#### Basic Usage
This indicator can check for all types of potential divergences: bullish, hidden bullish, bearish, hidden bearish. All you need to do is choose the type you want to check for under “DIVERGENCE TYPE” in the settings. On the chart, potential bullish divergences will show up as triangles below the price candles. one the chart potential bearish divergences will show up as upside down triangles above the price candles
#### Signals for Advanced Usage
You can use this indicator as a source in other indicators or strategies using the following information:
“ PD: Bull divergence signal ” will return “1” when a divergence is present and “0” when not present
“ PD: HBull divergence(hidden bull) signal ” will return “1” when a divergence is present and “0” when not present
“ PD: Bear divergence signal ” will return “1” when a divergence is present and “0” when not present
“ PD: HBear divergence(hidden bear) signal ” will return “1” when a divergence is present and “0” when not present
“ PD: enter ” signal will return a “1” when both the days and years criteria in the “entry filter settings” are met and “0” when not met.
“ PD: exit ” signal will return a “1” when the days criteria in the “exit filter settings” are met and “0” when not met.
#### Examples of Using Signals
1. If you are testing a long strategy for Bitcoin and do not want it to run during bear market years(e.g., the second year after a US presidential election), you can enable the “year and day filter for entry,” uncheck the following years in the settings: 2010, 2014, 2018, 2022, 2026, and reference the signal below in our strategy
signal: “ PD: enter ”
2. Let’s say you have a good long strategy, but want to make it a bit more profitable, you can tell the strategy not to run on days where there is potential bearish divergence and have it only run on more profitable days using these signals and the appropriate settings in the indicator
signal: “ PD: Bear divergence signal ” will return a ‘0’ with no bearish divergence present
signal: “ PD: enter ” will return a “1” if the entry falls on a specific, more profitable day chosen in the settings
#### Disclaimer
The "Potential Divergence Checker" indicator is a tool designed to identify potential market signals. It may have bugs and not do what it should do. It is not a guarantee of future trading performance, and users should exercise caution when making trading decisions based on its outputs. Always perform your own research and consider consulting with a financial advisor before making any investment decisions. Trading involves significant risk, and past performance is not indicative of future results.
Averaging Down Strategy1. Averaging Down:
Definition: "Averaging Down" is a strategy in which an investor buys more shares of a declining asset, thus lowering the average purchase price. The main idea is that, by averaging down, the investor can recover faster when the price eventually rebounds.
Risk Considerations: This strategy assumes that the asset will recover in value. If the price continues to decline, however, the investor may suffer larger losses. Academic research highlights the psychological bias of loss aversion that often leads investors to engage in averaging down, despite the increased risk (Barberis & Huang, 2001).
2. RSI (Relative Strength Index):
Definition: The RSI is a momentum oscillator that measures the speed and change of price movements. It ranges from 0 to 100 and is commonly used to identify overbought or oversold conditions. A reading below 30 (or in this case, 35) typically indicates an oversold condition, which might suggest a potential buying opportunity (Wilder, 1978).
Risk Considerations: RSI-based strategies can produce many false signals in range-bound or choppy markets, where prices do not exhibit strong trends. This can lead to multiple losing trades and an overall negative performance (Gencay, 1998).
3. Combination of RSI and Price Movement:
Approach: The combination of RSI for entry signals and price movement (previous day's high) for exit signals aims to capture short-term market reversals. This hybrid approach attempts to balance momentum with price confirmation.
Risk Considerations: While this combination can work well in trending markets, it may struggle in volatile or sideways markets. Additionally, a significant risk of averaging down is that the trader may continue adding to a losing position, which can exacerbate losses if the price keeps falling.
Risk Warnings:
Increased Losses Through Averaging Down:
Averaging down involves buying more of a falling asset, which can increase exposure to downside risk. Studies have shown that this approach can lead to larger losses when markets continue to decline, especially during prolonged bear markets (Statman, 2004).
A key risk is that this strategy may lead to significant capital drawdowns if the price of the asset does not recover as expected. In the worst-case scenario, this can result in a total loss of the invested capital.
False Signals with RSI:
RSI-based strategies are prone to generating false signals, particularly in markets that do not exhibit strong trends. For example, Gencay (1998) found that while RSI can be effective in certain conditions, it often fails in choppy or range-bound markets, leading to frequent stop-outs and drawdowns.
Psychological Bias:
Behavioral finance research suggests that the "Averaging Down" strategy may be influenced by loss aversion, a bias where investors prefer to avoid losses rather than achieve gains (Kahneman & Tversky, 1979). This can lead to poor decision-making, as investors continue to add to losing positions in the hope of a recovery.
Empirical Studies:
Gencay (1998): The study "The Predictability of Security Returns with Simple Technical Trading Rules" found that technical indicators like RSI can provide predictive value in certain markets, particularly in volatile environments. However, they are less reliable in markets that lack clear trends.
Barberis & Huang (2001): Their research on behavioral biases, including loss aversion, explains why investors are often tempted to average down despite the risks, as they attempt to avoid realizing losses.
Statman (2004): In "The Diversification Puzzle," Statman discusses how strategies like averaging down can increase risk exposure without necessarily improving long-term returns, especially if the underlying asset continues to perform poorly.
Conclusion:
The "Averaging Down Strategy with RSI" combines elements of technical analysis with a psychologically-driven averaging down approach. While the strategy may offer opportunities in trending or oversold markets, it carries significant risks, particularly in volatile or declining markets. Traders should be cautious when using this strategy, ensuring they manage risk effectively and avoid overexposure to a losing position.
Relative Rating Index (RRI)The technical rating is one of the most perfect indicators. The reason is that this indicator is based on a majority vote of multiple indicators. It is logical that the judgment based on a majority vote of multiple indicators would not be inferior to the judgment made using only a single indicator. However, just as any indicator has its shortcomings, the technical rating also has weaknesses. The most significant issue is that it primarily provides only a momentary evaluation of the current situation.
Let's consider this in more detail. In the technical rating, an evaluation of 1.0 by the majority vote of indicators is considered a strong buy. However, in the market, there are naturally varying levels of strength. For example, would a market that only once reached an evaluation of 1.0 within a given period be considered the same as a market that consistently maintains an evaluation of 1.0? The latter clearly shows a stronger trend, but the technical rating does not provide an objective criterion for such differentiation. While it is possible to check the histogram to see how long the buy or sell rating has continued, there is no objective standard for judgment.
The indicator I have created this time compensates for this weakness by using the concept of RSI. As is well known, RSI is an indicator that shows the momentum of the market. RSI typically calculates the strength of the price increase during a 14-period by dividing the total upward movement by the total movement range. Similarly, I thought that if we divide the positive evaluations of the technical rating during a given period by the total evaluations, we could calculate the "momentum of the technical rating," which shows how often positive ratings have appeared during that period.
Below is the calculation formula.
1. Setting the Evaluation Period
Decide the period to calculate (e.g., 14 periods). This is denoted as `n`.
2. Total Positive Ratings of the Technical Rating
Calculate the total number of times the technical rating is evaluated as "strong buy" or "buy" during each period. This is called `positive_sum`.
3. Total Ratings
Count the total number of ratings (including buy, sell, and neutral) during the period. This is called `total_sum`.
4. Calculating the Upward Strength
Divide `positive_sum` by `total_sum` to calculate the ratio of positive ratings in the technical rating. This is called the "ratio of positive ratings."
The ratio of positive ratings, denoted as `P`, is calculated as follows:
P = positive_sum / total_sum
5. Calculating RRI
Following the calculation method of RSI, RRI is calculated by the following formula:
RRI = 100 - (100 / (1 + (P / (1 - P))))
As you can see, the calculation method is similar to that of RSI. Therefore, initially, I intended to name this indicator the Technical Rating RSI. However, RSI calculates based on the difference between the previous bar's price and the current bar's price, whereas this indicator calculates by summing the values of the technical ratings themselves. In the case of prices, if the difference between bars is zero, it indicates a flat market, but in the case of technical rating values, if 1.0 continues for two consecutive periods, it signifies an extremely strong buy rather than a flat market. For this reason, I decided that the calculation method could no longer be considered the same as the traditional RSI, and to avoid confusion, I chose to give this new indicator the name "Relative Rating Index" (RRI), as it provides a new type of numerical evaluation.
The information provided by this indicator is simple. When RRI exceeds 50, it means that more than 50% of the technical rating evaluations during the set period (I recommend 50 periods, but please determine the optimal value based on your timeframe) are buy evaluations. However, since there may be many false signals around exactly 50, I define it as buy-dominant when the value exceeds 60 and sell-dominant when it falls below 40. Additionally, if the graph itself is rising, it indicates that the buying momentum is strengthening, and if it is falling, it indicates that the selling momentum is increasing.
Furthermore, there are lines drawn at 90 and 10, but please note that unlike RSI, these do not indicate overbought or oversold conditions. When RRI exceeds 90, it means that over 90% of the technical rating evaluations during the specified period are buy evaluations, indicating an ongoing extremely strong buy trend. Until the RRI graph turns downward and falls below 90, it should rather be considered a buying opportunity.
With this new indicator, the technical rating becomes an indicator with depth, providing evaluations not only for the moment but over a specified period. I hope you find it helpful in your market analysis.
